Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. For example, hyperspectral imagers may be configured to collect and distinguish electromagnetic energy from a plurality of wavelength bands, such as those corresponding to ultraviolet, visible, and infrared light. Typically, hyperspectral imaging sensors collect hyperspectral image data as a set of images of a scene. In some embodiments, each image includes information in two spatial dimensions (i.e. “X” and “Y” dimensions) of the scene for a different wavelength range, or spectral band, of the electromagnetic spectrum. As shown in FIG. 1, multiple images, showing the spatial dimensions across a plurality of wavelength ranges, can be combined to form a three dimensional datacube for processing and analysis, where the spectral information forms the third dimension (i.e. a “Z” dimension). In some embodiments, the spectral information from each image is recorded as a spectral vector associated with each X/Y spatial coordinate from the spatial dimensions. Thus, a hyperspectral data cube may span in two spatial dimensions and one spectral dimension.
Hyperspectral imaging captures many spatial images, each associated with a relatively narrow spectral band, over a contiguous spectral range. As such, this may produce a spectrum associated with each pixel in the scene. For example, a sensor configured to receive twenty spectral bands might be considered hyperspectral when it covers a narrow range from 500 to 700 nm with twenty 10-nm-wide bands that span the entire 500-700 nm range. Conversely, a sensor that images twenty discrete bands within a wide spectral range (i.e. where wavelength gaps separate the twenty discrete bands through the spectral range) would be considered a multispectral sensor.
The precision of hyperspectral imaging sensors may be measured in spectral resolution (i.e. the width of each band of the spectrum that is captured). In some embodiments, sensitivity to a larger number of relatively narrower spectral bands may facilitate identification of objects of interest even if those objects are only captured in a handful of pixels. However, spatial resolution is a factor in addition to spectral resolution. If the pixels are spatially too large, then multiple objects can be captured in the same pixel and become difficult to identify. If the pixels are spatially too small, then signals from one object can spread over multiple pixels, which reduce the intensity of the signals from the object on each pixel, reduce the signal-to-noise ratio, and deteriorate the reliability of object identification. Any number of optical systems may be associated with a hyperspectral imager so as to increase the optical system's ability to identify the objects of interest.
As discussed in U.S. patent application Ser. No. 12/466,191, incorporated herein by reference in its entirety, it may be advantageous to facilitate detection of small targets using hyperspectral imaging systems utilizing optics with a reduced aperture size. For example, where hyperspectral imagers are mounted on air-based or space-based platforms, the aperture size of the optics on such platforms may greatly increase the overall cost of such systems. Thus, analytical algorithms or other processing techniques may be utilized with a hyperspectral imaging sensor so as to compensate for a reduction in optics, which may reduce the weight and/or cost of the hyperspectral imaging system.
Many conventional hyperspectral imaging sensors are designed to utilize a small f-number (a large f-cone) and attempt to limit the blur size to the width of a spatial pixel to maximize the signal-to-noise ratio for each pixel. Such design characteristics typically utilize a large telescope aperture or a large physical pixel on the hyperspectral sensor. While large apertures increase the weight, as discussed above, large physical pixels in the hyperspectral sensor result in large ground sample distances (GSDs) when projected on the ground. The GSD is the correspondence of each pixel to the area resolved. For example, a satellite based sensor may have a 1 meter GSD, meaning that each resolved pixel corresponds to 1 square meter of ground. As such, a large GSD resulting from large physical pixels negatively affects the ability of hyperspectral imaging systems to detect target objects, as multiple targets may be blended into a single pixel.
Some conventional hyperspectral imaging systems are able to detect targets whose dimension is comparable to or smaller than a spatial pixel. For example, some hyperspectral imaging sensors may be configured so that the optical blur, also known as the point spread function (PSF), is smaller than a pixel on the sensor. Where such sensors are utilized for high quality single-color imaging, the blur typically spans several pixels. Such systems may rely on spatial contrast of edges and texture for target detection, such as by comparing the spectral contrast of a target to its surroundings. However, spectral contrast degrades when optical blur is much larger than a pixel, and little energy from a target is captured on any given pixel.
Accordingly, it may be appreciated that where the blur of an optical system is greater than the size of a pixel (either by design or unintentionally), image processing may be utilized to enhance analysis of the hyperspectral image data, facilitating target detection on detectors where low energy is found on any given pixel.
One such image processing technique, entitled Adaptive Spatial-Spectral Processing (“ASSP”), is described in U.S. patent application Ser. No. 12/466,191, incorporated herein in its entirety by reference. ASSP operates on the output of a spectral filter (i.e. a spectral matched filter) which has converted a hyperspectral data cube to a scalar image, where a value at each pixel represents a detection score. By utilizing an adaptive set of weighting to aggregate target energy distributed around each pixel, a ratio of signal-to-clutter (SCR) may be improved. With ASSP, a weighted average of several pixels in the detected image is taken, and the weights are adapted for the ambient clutter levels. However, the signals across multiple pixels that are aggregated in ASSP by using weighted sums are the product of a fixed set of weights developed assuming a target is centered in a pixel, and an adaptive set of weights that change with scene clutter. As such, no consideration is made in ASSP to account for, among other things, non-centered positions of target signals across a plurality of pixels. Although the improvement of the SCR from ASSP is significant (i.e. from approximately a 20% SCR to approximately a 50% SCR), further improvement is desirable.